mirror of
https://github.com/vale981/ray
synced 2025-03-09 04:46:38 -04:00
105 lines
4 KiB
YAML
105 lines
4 KiB
YAML
####################################################################
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# All nodes in this cluster will auto-terminate in 1 hour
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####################################################################
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# An unique identifier for the head node and workers of this cluster.
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cluster_name: ray-rllib-stress-tests
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# The minimum number of workers nodes to launch in addition to the head
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# node. This number should be >= 0.
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min_workers: 9
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# The maximum number of workers nodes to launch in addition to the head
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# node. This takes precedence over min_workers.
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max_workers: 9
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# The autoscaler will scale up the cluster to this target fraction of resource
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# usage. For example, if a cluster of 10 nodes is 100% busy and
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# target_utilization is 0.8, it would resize the cluster to 13. This fraction
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# can be decreased to increase the aggressiveness of upscaling.
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# This value must be less than 1.0 for scaling to happen.
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target_utilization_fraction: 0.8
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# If a node is idle for this many minutes, it will be removed.
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idle_timeout_minutes: 5
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# Cloud-provider specific configuration.
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provider:
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type: aws
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region: us-west-2
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availability_zone: us-west-2a
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cache_stopped_nodes: False
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# How Ray will authenticate with newly launched nodes.
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auth:
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ssh_user: ubuntu
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# By default Ray creates a new private keypair, but you can also use your own.
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# If you do so, make sure to also set "KeyName" in the head and worker node
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# configurations below.
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# ssh_private_key: /path/to/your/key.pem
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# Provider-specific config for the head node, e.g. instance type. By default
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# Ray will auto-configure unspecified fields such as SubnetId and KeyName.
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# For more documentation on available fields, see:
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# http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances
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head_node:
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InstanceType: p3.16xlarge
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ImageId: ami-07728e9e2742b0662 # Deep Learning AMI (Ubuntu 16.04)
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# Set primary volume to 25 GiB
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BlockDeviceMappings:
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- DeviceName: /dev/sda1
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Ebs:
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VolumeSize: 100
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# Additional options in the boto docs.
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# Provider-specific config for worker nodes, e.g. instance type. By default
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# Ray will auto-configure unspecified fields such as SubnetId and KeyName.
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# For more documentation on available fields, see:
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# http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances
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worker_nodes:
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InstanceType: m4.16xlarge
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ImageId: ami-07728e9e2742b0662 # Deep Learning AMI (Ubuntu 16.04)
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# Set primary volume to 25 GiB
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BlockDeviceMappings:
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- DeviceName: /dev/sda1
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Ebs:
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VolumeSize: 100
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# Run workers on spot by default. Comment this out to use on-demand.
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# InstanceMarketOptions:
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# MarketType: spot
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# Additional options can be found in the boto docs, e.g.
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# SpotOptions:
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# MaxPrice: MAX_HOURLY_PRICE
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# Additional options in the boto docs.
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# Files or directories to copy to the head and worker nodes. The format is a
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# dictionary from REMOTE_PATH: LOCAL_PATH, e.g.
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file_mounts: {
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# "/path1/on/remote/machine": "/path1/on/local/machine",
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# "/path2/on/remote/machine": "/path2/on/local/machine",
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}
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# List of shell commands to run to set up nodes.
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setup_commands: []
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# Custom commands that will be run on the head node after common setup.
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head_setup_commands: []
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# Custom commands that will be run on worker nodes after common setup.
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worker_setup_commands: []
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# Command to start ray on the head node. You don't need to change this.
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head_start_ray_commands:
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- source activate tensorflow_p36 && ray stop
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- ulimit -n 65536; source activate tensorflow_p36 && OMP_NUM_THREADS=1 ray start --head --port=6379 --object-manager-port=8076 --autoscaling-config=~/ray_bootstrap_config.yaml
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# Command to start ray on worker nodes. You don't need to change this.
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worker_start_ray_commands:
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- source activate tensorflow_p36 && ray stop
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- ulimit -n 65536; source activate tensorflow_p36 && OMP_NUM_THREADS=1 ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076
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